Estimation of semiparametric varying-coefficient spatial autoregressive models with missing in the dependent variable

다국어 초록 (Multilingual Abstract)

This paper investigates estimation of semiparametric varying-coefcient spatial autoregressive models in which the dependent variable is missing at random. An inverse propensity score weighted sieve two-stage least squares (S-2SLS) estimation with imputation is proposed. The proposed estimators are shown to be consistent, no matter the initial value is taken as the naive S-2SLS estimate or the naive nonlinear least squares estimate, and the asymptotic distribution of the latter is also derived.
Simulation studies are carried out to investigate the performance of the proposed estimator. The method is fnally exemplifed with one real data set on Boston housing prices.

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